{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "41e1f056",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import PolynomialFeatures, MinMaxScaler, StandardScaler\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz, export_text\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "649cd58c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Hotel_Reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ff0b1e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(index = list(range(data.shape[0] // 16, data.shape[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c39fa23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hotel_Address</th>\n",
       "      <th>Additional_Number_of_Scoring</th>\n",
       "      <th>Review_Date</th>\n",
       "      <th>Average_Score</th>\n",
       "      <th>Hotel_Name</th>\n",
       "      <th>Reviewer_Nationality</th>\n",
       "      <th>Negative_Review</th>\n",
       "      <th>Review_Total_Negative_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews</th>\n",
       "      <th>Positive_Review</th>\n",
       "      <th>Review_Total_Positive_Word_Counts</th>\n",
       "      <th>Total_Number_of_Reviews_Reviewer_Has_Given</th>\n",
       "      <th>Reviewer_Score</th>\n",
       "      <th>Tags</th>\n",
       "      <th>days_since_review</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Russia</td>\n",
       "      <td>I am so angry that i made this post available...</td>\n",
       "      <td>397</td>\n",
       "      <td>1403</td>\n",
       "      <td>Only the park outside of the hotel was beauti...</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>8/3/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>No Negative</td>\n",
       "      <td>0</td>\n",
       "      <td>1403</td>\n",
       "      <td>No real complaints the hotel was great great ...</td>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Duplex Double...</td>\n",
       "      <td>0 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Rooms are nice but for elderly a bit difficul...</td>\n",
       "      <td>42</td>\n",
       "      <td>1403</td>\n",
       "      <td>Location was good and staff were ok It is cut...</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>[' Leisure trip ', ' Family with young childre...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/31/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>My room was dirty and I was afraid to walk ba...</td>\n",
       "      <td>210</td>\n",
       "      <td>1403</td>\n",
       "      <td>Great location in nice surroundings the bar a...</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>[' Leisure trip ', ' Solo traveler ', ' Duplex...</td>\n",
       "      <td>3 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s Gravesandestraat 55 Oost 1092 AA Amsterdam ...</td>\n",
       "      <td>194</td>\n",
       "      <td>7/24/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>Hotel Arena</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>You When I booked with your company on line y...</td>\n",
       "      <td>140</td>\n",
       "      <td>1403</td>\n",
       "      <td>Amazing location and building Romantic setting</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.7</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Suite ', ' St...</td>\n",
       "      <td>10 days</td>\n",
       "      <td>52.360576</td>\n",
       "      <td>4.915968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Hotel_Address  \\\n",
       "0   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "1   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "2   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "3   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "4   s Gravesandestraat 55 Oost 1092 AA Amsterdam ...   \n",
       "\n",
       "   Additional_Number_of_Scoring Review_Date  Average_Score   Hotel_Name  \\\n",
       "0                           194    8/3/2017            7.7  Hotel Arena   \n",
       "1                           194    8/3/2017            7.7  Hotel Arena   \n",
       "2                           194   7/31/2017            7.7  Hotel Arena   \n",
       "3                           194   7/31/2017            7.7  Hotel Arena   \n",
       "4                           194   7/24/2017            7.7  Hotel Arena   \n",
       "\n",
       "  Reviewer_Nationality                                    Negative_Review  \\\n",
       "0              Russia    I am so angry that i made this post available...   \n",
       "1             Ireland                                         No Negative   \n",
       "2           Australia    Rooms are nice but for elderly a bit difficul...   \n",
       "3      United Kingdom    My room was dirty and I was afraid to walk ba...   \n",
       "4         New Zealand    You When I booked with your company on line y...   \n",
       "\n",
       "   Review_Total_Negative_Word_Counts  Total_Number_of_Reviews  \\\n",
       "0                                397                     1403   \n",
       "1                                  0                     1403   \n",
       "2                                 42                     1403   \n",
       "3                                210                     1403   \n",
       "4                                140                     1403   \n",
       "\n",
       "                                     Positive_Review  \\\n",
       "0   Only the park outside of the hotel was beauti...   \n",
       "1   No real complaints the hotel was great great ...   \n",
       "2   Location was good and staff were ok It is cut...   \n",
       "3   Great location in nice surroundings the bar a...   \n",
       "4    Amazing location and building Romantic setting    \n",
       "\n",
       "   Review_Total_Positive_Word_Counts  \\\n",
       "0                                 11   \n",
       "1                                105   \n",
       "2                                 21   \n",
       "3                                 26   \n",
       "4                                  8   \n",
       "\n",
       "   Total_Number_of_Reviews_Reviewer_Has_Given  Reviewer_Score  \\\n",
       "0                                           7             2.9   \n",
       "1                                           7             7.5   \n",
       "2                                           9             7.1   \n",
       "3                                           1             3.8   \n",
       "4                                           3             6.7   \n",
       "\n",
       "                                                Tags days_since_review  \\\n",
       "0  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "1  [' Leisure trip ', ' Couple ', ' Duplex Double...            0 days   \n",
       "2  [' Leisure trip ', ' Family with young childre...            3 days   \n",
       "3  [' Leisure trip ', ' Solo traveler ', ' Duplex...            3 days   \n",
       "4  [' Leisure trip ', ' Couple ', ' Suite ', ' St...           10 days   \n",
       "\n",
       "         lat       lng  \n",
       "0  52.360576  4.915968  \n",
       "1  52.360576  4.915968  \n",
       "2  52.360576  4.915968  \n",
       "3  52.360576  4.915968  \n",
       "4  52.360576  4.915968  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f7c41ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2014 entries, 0 to 2013\n",
      "Data columns (total 17 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   Hotel_Address                               2014 non-null   object \n",
      " 1   Additional_Number_of_Scoring                2014 non-null   int64  \n",
      " 2   Review_Date                                 2014 non-null   object \n",
      " 3   Average_Score                               2014 non-null   float64\n",
      " 4   Hotel_Name                                  2014 non-null   object \n",
      " 5   Reviewer_Nationality                        2014 non-null   object \n",
      " 6   Negative_Review                             2014 non-null   object \n",
      " 7   Review_Total_Negative_Word_Counts           2014 non-null   int64  \n",
      " 8   Total_Number_of_Reviews                     2014 non-null   int64  \n",
      " 9   Positive_Review                             2014 non-null   object \n",
      " 10  Review_Total_Positive_Word_Counts           2014 non-null   int64  \n",
      " 11  Total_Number_of_Reviews_Reviewer_Has_Given  2014 non-null   int64  \n",
      " 12  Reviewer_Score                              2014 non-null   float64\n",
      " 13  Tags                                        2014 non-null   object \n",
      " 14  days_since_review                           2014 non-null   object \n",
      " 15  lat                                         2014 non-null   float64\n",
      " 16  lng                                         2014 non-null   float64\n",
      "dtypes: float64(4), int64(5), object(8)\n",
      "memory usage: 267.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22da6482",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Признаки, имеющие максимальную по модулю корреляцию со средней оценкой\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\noski\\AppData\\Local\\Temp\\ipykernel_10740\\955532171.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  best_params = data.corr()['Average_Score'].map(abs).sort_values(ascending=False)[1:]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Total_Number_of_Reviews         0.928197\n",
       "Additional_Number_of_Scoring    0.888086\n",
       "lng                             0.843443\n",
       "lat                             0.838948\n",
       "Reviewer_Score                  0.399864\n",
       "Name: Average_Score, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Признаки, имеющие максимальную по модулю корреляцию со средней оценкой')\n",
    "best_params = data.corr()['Average_Score'].map(abs).sort_values(ascending=False)[1:]\n",
    "best_params = best_params[best_params.values > 0.3]\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c43fba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "sns.heatmap(data[best_params.index].corr(), vmin=-1, vmax=1, cmap='coolwarm', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4378accb",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = best_params.drop(['Additional_Number_of_Scoring', 'Reviewer_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64a04311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "sns.heatmap(data[best_params.index].corr(), vmin=-1, vmax=1, cmap='coolwarm', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3166b28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 3))\n",
    "sns.heatmap(pd.DataFrame(data[np.append(best_params.index.values, 'Average_Score')].corr()['Average_Score'].sort_values(ascending=False)[1:]), vmin=-1, vmax=1, cmap='coolwarm', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ae01c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Average_Score']\n",
    "X = data[best_params.index]\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bf98da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def print_metrics(y_test, y_pred):\n",
    "    print(f\"R^2: {r2_score(y_test, y_pred)}\")\n",
    "    print(f\"MSE: {mean_squared_error(y_test, y_pred)}\")\n",
    "    print(f\"MAE: {mean_absolute_error(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8f551002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 1.0\n",
      "MSE: 1.7758746664271208e-22\n",
      "MAE: 1.2980950749569883e-11\n"
     ]
    }
   ],
   "source": [
    "linear_model = LinearRegression()\n",
    "linear_model.fit(x_train, y_train)\n",
    "y_pred_linear = linear_model.predict(x_test)\n",
    "print_metrics(y_test, y_pred_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30f2537d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 1.0\n",
      "MSE: 3.8398489110352158e-28\n",
      "MAE: 1.9514968153840933e-14\n"
     ]
    }
   ],
   "source": [
    "poly_model = PolynomialFeatures(degree=3)\n",
    "x_train_poly = poly_model.fit_transform(x_train)\n",
    "x_test_poly = poly_model.fit_transform(x_test)\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(x_train_poly, y_train)\n",
    "y_pred_poly = linear_model.predict(x_test_poly)\n",
    "print_metrics(y_test, y_pred_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ea0c87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Number_of_Reviews</th>\n",
       "      <th>lng</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.409000e+03</td>\n",
       "      <td>1.409000e+03</td>\n",
       "      <td>1.409000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.512866e-16</td>\n",
       "      <td>1.008577e-16</td>\n",
       "      <td>-1.939999e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000355e+00</td>\n",
       "      <td>1.000355e+00</td>\n",
       "      <td>1.000355e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.475336e+00</td>\n",
       "      <td>-5.157300e-01</td>\n",
       "      <td>-5.297420e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.483478e-01</td>\n",
       "      <td>-5.157300e-01</td>\n",
       "      <td>-5.297420e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.742373e-01</td>\n",
       "      <td>-4.724665e-01</td>\n",
       "      <td>-4.649067e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.742373e-01</td>\n",
       "      <td>-4.724665e-01</td>\n",
       "      <td>-4.649067e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.276867e+00</td>\n",
       "      <td>2.048855e+00</td>\n",
       "      <td>2.048415e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total_Number_of_Reviews           lng           lat\n",
       "count             1.409000e+03  1.409000e+03  1.409000e+03\n",
       "mean              1.512866e-16  1.008577e-16 -1.939999e-14\n",
       "std               1.000355e+00  1.000355e+00  1.000355e+00\n",
       "min              -1.475336e+00 -5.157300e-01 -5.297420e-01\n",
       "25%              -6.483478e-01 -5.157300e-01 -5.297420e-01\n",
       "50%               8.742373e-01 -4.724665e-01 -4.649067e-01\n",
       "75%               8.742373e-01 -4.724665e-01 -4.649067e-01\n",
       "max               4.276867e+00  2.048855e+00  2.048415e+00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler().fit(x_train)\n",
    "x_train_scaled = pd.DataFrame(scaler.transform(x_train), columns=x_train.columns)\n",
    "x_test_scaled = pd.DataFrame(scaler.transform(x_test), columns=x_train.columns)\n",
    "x_train_scaled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67382c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 14.0}\n"
     ]
    }
   ],
   "source": [
    "params = {'C': np.concatenate([np.arange(0.1, 2, 0.1), np.arange(2, 15, 1)])}\n",
    "svm_model = SVR(kernel='linear')\n",
    "grid_cv = GridSearchCV(estimator=svm_model, param_grid=params, cv=10, n_jobs=-1, scoring='r2')\n",
    "grid_cv.fit(x_train_scaled, y_train)\n",
    "print(grid_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96011963",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9427495224030507\n",
      "MSE: 0.020600515095923963\n",
      "MAE: 0.10359069139015793\n"
     ]
    }
   ],
   "source": [
    "best_svm_model = grid_cv.best_estimator_\n",
    "best_svm_model = SVR(kernel='linear', C=14)\n",
    "best_svm_model.fit(x_train_scaled, y_train)\n",
    "y_pred_svm = best_svm_model.predict(x_test_scaled)\n",
    "print_metrics(y_test, y_pred_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98d10162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_samples_leaf': 3}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "params = {'min_samples_leaf': range(3, 30)}\n",
    "tree = DecisionTreeRegressor(random_state=3)\n",
    "grid_cv = GridSearchCV(estimator=tree, cv=5, param_grid=params, n_jobs=-1, scoring='neg_mean_absolute_error')\n",
    "grid_cv.fit(x_train, y_train)\n",
    "print(grid_cv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f411b92c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 1.0\n",
      "MSE: 6.910909189204745e-27\n",
      "MAE: 6.44979019687691e-14\n"
     ]
    }
   ],
   "source": [
    "\n",
    "best_tree = grid_cv.best_estimator_\n",
    "best_tree.fit(x_train, y_train)\n",
    "y_pred_tree = best_tree.predict(x_test)\n",
    "print_metrics(y_test, y_pred_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "044b7b66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Важность признаков в дереве решений\n",
      "\n",
      "Total_Number_of_Reviews: 0.782\n",
      "lat: 0.218\n",
      "lng: 0.0\n"
     ]
    }
   ],
   "source": [
    "importances = pd.DataFrame(data=zip(x_train.columns, best_tree.feature_importances_), columns=['Признак', 'Важность'])\n",
    "print('Важность признаков в дереве решений\\n')\n",
    "for row in importances.sort_values(by='Важность', ascending=False).values:\n",
    "    print(f'{row[0]}: {round(row[1], 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "26b1354e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "sns.barplot(data=importances.sort_values(by='Важность', ascending=False), y='Признак', x='Важность', orient='h', )\n",
    "plt.title('Важность признаков в дереве решений')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "70959f71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Линейная регрессия\n",
      "R^2: 1.0\n",
      "MSE: 1.7758746664271208e-22\n",
      "MAE: 1.2980950749569883e-11\n",
      "\n",
      "Полиномиальная регрессия\n",
      "R^2: 1.0\n",
      "MSE: 3.8398489110352158e-28\n",
      "MAE: 1.9514968153840933e-14\n",
      "\n",
      "Метод опорных векторов\n",
      "R^2: 0.9427495224030507\n",
      "MSE: 0.020600515095923963\n",
      "MAE: 0.10359069139015793\n",
      "\n",
      "Дерево решений\n",
      "R^2: 1.0\n",
      "MSE: 6.910909189204745e-27\n",
      "MAE: 6.44979019687691e-14\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('Линейная регрессия')\n",
    "print_metrics(y_test, y_pred_linear)\n",
    "\n",
    "print('\\nПолиномиальная регрессия')\n",
    "print_metrics(y_test, y_pred_poly)\n",
    "\n",
    "print('\\nМетод опорных векторов')\n",
    "print_metrics(y_test, y_pred_svm)\n",
    "\n",
    "print('\\nДерево решений')\n",
    "print_metrics(y_test, y_pred_tree)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
